The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

By Radhakrishnan Mahadevan, C. Schilling. Metabolic Engineering, 2003

1,236 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6d63e941331560535350c0d9275ce7c8e761253e

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Citations in the corpus

Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis (2010, methodology): “The systemic reaction V max is estimated by ﬂ ux variability analysis (FVA) (21) ( SI Methods ) on the unregulated metabolic model.”
Constraint-based models predict metabolic and associated cellular functions (2014, background): “Another common approach is flux variability analysis 105 , in which the maximum and minimum fluxes through each reaction are iteratively computed when the flux of the objective function is typically constrained to its maximum value (shown by yellow circles).”
Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “Reactions that have a non-overlapping FVA range must be different be-tween the two states and can be indicative of important metabolic changes.”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “Flux variability analysis: identiﬁcation of reactions that are predicted to never carry any ﬂux Mahadevan & Schilling (2003) Identiﬁcation of dead-end metabolites, which can never be produced or consumed.”
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information (2007, methodology): “%) on each of 174 carbon sources using FVA (Mahadevan and Schilling, 2003) and the BOFCORE.”
Thermodynamics-based metabolic flux analysis. (2007, methodology): “Flux variability analysis (FVA) (53) was used to classify the reactions involved in the maximum production of biomass from glucose in E.”
Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017, methodology): “Furthermore, the resulting enzyme-constrained models have the same structure as any GEM, such that it can be used for any constrained-based analysis method (e.g., FBA, FVA, random sampling), and it can do so in similar computational times compared ª 2017 The Authors Molecular Systems Biology 13: 935”
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 (2007, neutral): “This is carried out by optimizing for a particular objective, while still satisfying the given constraints set on the syste”
An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. (2006, methodology): “A linear programming formulation, referred as the min/max problem (Burgard et al., 2001; Mahadevan and Schilling, 2003) is solved for base flux estimation for reactions in the most recent genome-scale model of E.”
OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (2010, background): “This maximal range of flux variability for the wild-type strain can be elucidated by iteratively maximizing and minimizing each flux [20,43] subject to the stoichiometric constraints, uptake conditions and MFA flux data (either exact values or ranges) whenever available for the wild-type strain.”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “To avoid the well-known degeneracy problem in FBA solutions [61] the option to minimize the Manhattan norm of the flux distribution was selected (according to the principle of parsimonious enzyme usage [62]).”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, methodology): “Because constraints-based models are often underdetermined they may provide multiple mathematically-equivalent solutions to a specific question – these equivalent solutions must be assessed with experimental data for biological relevance [18].”